I need your help!
I want your feedback to make the book better for you and other readers. If you find typos, errors, or places where the text may be improved, please let me know. The best ways to provide feedback are by GitHub or hypothes.is annotations.
You can leave a comment at the bottom of the page/chapter, or open an issue or submit a pull request on GitHub: https://github.com/isaactpetersen/Fantasy-Football-Analytics-Textbook
Alternatively, you can leave an annotation using hypothes.is.
To add an annotation, select some text and then click the
symbol on the pop-up menu.
To see the annotations of others, click the
symbol in the upper right-hand corner of the page.
19 Machine Learning
19.1 Getting Started
19.1.1 Load Packages
19.1.2 Load Data
Code
# Downloaded Data - Processed
load(file = "./data/nfl_players.RData")
load(file = "./data/nfl_teams.RData")
load(file = "./data/nfl_rosters.RData")
load(file = "./data/nfl_rosters_weekly.RData")
load(file = "./data/nfl_schedules.RData")
load(file = "./data/nfl_combine.RData")
load(file = "./data/nfl_draftPicks.RData")
load(file = "./data/nfl_depthCharts.RData")
load(file = "./data/nfl_pbp.RData")
load(file = "./data/nfl_4thdown.RData")
load(file = "./data/nfl_participation.RData")
#load(file = "./data/nfl_actualFantasyPoints_weekly.RData")
load(file = "./data/nfl_injuries.RData")
load(file = "./data/nfl_snapCounts.RData")
load(file = "./data/nfl_espnQBR_seasonal.RData")
load(file = "./data/nfl_espnQBR_weekly.RData")
load(file = "./data/nfl_nextGenStats_weekly.RData")
load(file = "./data/nfl_advancedStatsPFR_seasonal.RData")
load(file = "./data/nfl_advancedStatsPFR_weekly.RData")
load(file = "./data/nfl_playerContracts.RData")
load(file = "./data/nfl_ftnCharting.RData")
load(file = "./data/nfl_playerIDs.RData")
load(file = "./data/nfl_rankings_draft.RData")
load(file = "./data/nfl_rankings_weekly.RData")
load(file = "./data/nfl_expectedFantasyPoints_weekly.RData")
load(file = "./data/nfl_expectedFantasyPoints_pbp.RData")
# Calculated Data - Processed
load(file = "./data/nfl_actualStats_career.RData")
load(file = "./data/nfl_actualStats_seasonal.RData")
load(file = "./data/player_stats_weekly.RData")
load(file = "./data/player_stats_seasonal.RData")19.1.3 Specify Options
19.2 Overview of Machine Learning
Machine learning takes us away from focusing on causal inference. Machine learning does not care about which processes are causal—i.e., which processes influence the outcome. Instead, machine learning cares about prediction—it cares about a predictor variable to the extent that it increases predictive accuracy regardless of whether it is causally related to the outcome.
Machine learning can be useful for leveraging big data and lots of predictor variable to develop predictive models with greater accuracy. However, many machine learning techniques are black boxes—it is often unclear how or why certain predictions are made, which can make it difficult to interpret the model’s decisions and understand the underlying relationships between variables. Machine learning tends to be a data-driven, atheoretical technique. This can result in overfitting. Thus, when estimating machine learning models, it is common to keep a hold-out sample for use in cross-validation to evaluate the extent of shrinkage of model coefficients. The data that the model is trained on is known as the “training data”. The data that the model was not trained on but is then is independently tested on—i.e., the hold-out sample—is the “test data”. Shrinkage occurs when predictor variables explain some random error variance in the original model. When the model is applied to an independent sample (i.e., the test data), the predictive model will likely not perform quite as well, and the regressions coefficients will tend to get smaller (i.e., shrink).
If the test data were collected as part of the same processes as the original data and were merely held out for purposes of analysis, this is called internal cross-validation. If the test data were collected separately from the original data used to train the model, this is called external cross-validation.
Most machine learning methods were developed with cross-sectional data in mind. That is, they assume that each person has only one observation on the outcome variable. However, with longitudinal data, each person has multiple observations on the outcome variable.
When performing machine learning, various approaches may help address this:
- transform data from long to wide form, so that each person has only one row
- when designing the training and test sets, keep all measurements from the same person in the same data object (either the training or test set); do not have some measurements from a given person in the training set and other measurements from the same person in the test set
- use a machine learning approach that accounts for the clustered/nested nature of the data
19.3 Types of Machine Learning
There are many approaches to machine learning. This chapter discusses several key ones:
- supervised learning
- continuous outcome (i.e., regression)
- categorical outcome (i.e., classification)
- logistic regression
- support vector machine
- random forest
- boosting
- unsupervised learning
- semi-supervised learning
- reinforcement learning
- deep learning
- ensemble
Ensemble machine learning methods combine multiple machine learning approaches with the goal that combining multiple approaches might lead to more accurate predictions than any one method might be able to achieve on its own.
19.3.1 Supervised Learning
Supervised learning involves learning from data where the correct classification or outcome is known. For instance, predicting how many points a player will score is a supervised learning task, because there is a ground truth—the actual number of points scored—that can be used to train and evaluate the model.
Unlike linear and logistic regression, various machine learning techniques can handle multicollinearity, including lasso regression, ridge regression, and elastic net regression. Least absolute shrinkage and selection operator (lasso) regression performs selection of which predictor variables to keep in the model by shrinking some coefficients to zero, effectively removing them from the model. Ridge regression shrinks the coefficients of predictor variables toward zero, but not to zero, so it does not perform selection of which predictor variables to retain; this allows it to yield stable estimates for multiple correlated predictor variables in the context of multicollinearity. Elastic net involves a combination of lasso and ridge regression; it performs selection of which predictor variables to keep by shrinking the coefficients of some predictor variables to zero (like lasso, for variable selection), and it shrinks the coefficients of some predictor variables toward zero (like ridge, for handling multicollinearity among correlated predictors).
Unless interactions or nonlinear terms are specified, linear, logistic, lasso, ridge, and elastic net regression assume additive and linear associations between the predictors and outcome. That is, they do not automatically account for interactions among the predictor variables or for nonlinear associations between the predictor variables and the outcome variable (unless interaction terms or nonlinear transformations are explicitly included). By contrast, random forests and tree boosting methods automatically account for interactions and nonlinear associations between predictors and the outcome variable. These models recursively partition the data in ways that capture complex patterns without the need to manually specify interaction or polynomial terms.
19.3.2 Unsupervised Learning
Unsupervised learning involves learning from data without known classifications. Unsupervised learning is used to discover hidden patterns, groupings, or structures in the data. For instance, if we want to identify different subtypes of Wide Receivers based on their playing style or performance metrics, or uncover underlying dimensions in a large dataset, we would use an unsupervised learning approach.
We describe cluster analysis in Chapter 21. We describe principal component analysis in Chapter 23.
19.3.3 Semi-supervised Learning
Semi-supervised learning combines supervised learning and unsupervised learning by training the model on some data for which the classification is known and some data for which the classification is not known.
19.3.4 Reinforcement Learning
Reinforcement learning involves an agent learning to make decisions by interacting with the environment. Through trial and error, the agent receives feedback in the form of rewards or penalties and learns a strategy that maximizes the cumulative reward over time.
19.4 Data Processing
19.4.1 Prepare Data for Merging
Code
# Prepare data for merging
#-todo: calculate years_of_experience
## Use common name for the same (gsis_id) ID variable
#nfl_actualFantasyPoints_player_weekly <- nfl_actualFantasyPoints_player_weekly %>%
# rename(gsis_id = player_id)
#
#nfl_actualFantasyPoints_player_seasonal <- nfl_actualFantasyPoints_player_seasonal %>%
# rename(gsis_id = player_id)
player_stats_seasonal_offense <- player_stats_seasonal %>%
filter(position_group %in% c("QB","RB","WR","TE")) %>%
rename(gsis_id = player_id)
player_stats_weekly_offense <- player_stats_weekly %>%
filter(position_group %in% c("QB","RB","WR","TE")) %>%
rename(gsis_id = player_id)
nfl_expectedFantasyPoints_weekly <- nfl_expectedFantasyPoints_weekly %>%
rename(gsis_id = player_id)
## Rename other variables to ensure common names
## Ensure variables with the same name have the same type
nfl_players <- nfl_players %>%
mutate(
birth_date = as.Date(birth_date),
jersey_number = as.character(jersey_number),
gsis_it_id = as.character(gsis_it_id),
years_of_experience = as.integer(years_of_experience))
player_stats_seasonal_offense <- player_stats_seasonal_offense %>%
mutate(
birth_date = as.Date(birth_date),
jersey_number = as.character(jersey_number),
gsis_it_id = as.character(gsis_it_id))
nfl_rosters <- nfl_rosters %>%
mutate(
draft_number = as.integer(draft_number))
nfl_rosters_weekly <- nfl_rosters_weekly %>%
mutate(
draft_number = as.integer(draft_number))
nfl_depthCharts <- nfl_depthCharts %>%
mutate(
season = as.integer(season))
nfl_expectedFantasyPoints_weekly <- nfl_expectedFantasyPoints_weekly %>%
mutate(
season = as.integer(season),
receptions = as.integer(receptions)) %>%
distinct(gsis_id, season, week, .keep_all = TRUE) # drop duplicated rows
## Rename variables
nfl_draftPicks <- nfl_draftPicks %>%
rename(
games_career = games,
pass_completions_career = pass_completions,
pass_attempts_career = pass_attempts,
pass_yards_career = pass_yards,
pass_tds_career = pass_tds,
pass_ints_career = pass_ints,
rush_atts_career = rush_atts,
rush_yards_career = rush_yards,
rush_tds_career = rush_tds,
receptions_career = receptions,
rec_yards_career = rec_yards,
rec_tds_career = rec_tds,
def_solo_tackles_career = def_solo_tackles,
def_ints_career = def_ints,
def_sacks_career = def_sacks
)
## Subset variables
nfl_expectedFantasyPoints_weekly <- nfl_expectedFantasyPoints_weekly %>%
select(gsis_id:position, contains("_exp"), contains("_diff"), contains("_team")) #drop "raw stats" variables (e.g., rec_yards_gained) so they don't get coalesced with actual stats
# Check duplicate ids
player_stats_seasonal_offense %>%
group_by(gsis_id, season) %>%
filter(n() > 1) %>%
head()Code
Identify objects with shared variable names:
[1] "gsis_id" "position"[1] 21360[1] 2855[1] "gsis_id" "season" "team" "age" [1] 14859[1] 10395[1] 14858[1] 10395[1] 14859[1] 10325[1] "gsis_id" "season" "week" "position" "full_name"[1] 845134[1] 100272[1] 841942[1] 100272[1] 845101[1] 97815[1] 845118[1] 9781519.4.2 Merge Data
To merge data, we use the powerjoin package (Fabri, 2022):
Code
# Create lists of objects to merge, depending on data structure: id; or id-season; or id-season-week
#-todo: remove redundant variables
playerListToMerge <- list(
nfl_players %>% filter(!is.na(gsis_id)),
nfl_draftPicks %>% filter(!is.na(gsis_id)) %>% select(-season)
)
playerSeasonListToMerge <- list(
player_stats_seasonal_offense %>% filter(!is.na(gsis_id), !is.na(season)),
nfl_advancedStatsPFR_seasonal %>% filter(!is.na(gsis_id), !is.na(season))
)
playerSeasonWeekListToMerge <- list(
nfl_rosters_weekly %>% filter(!is.na(gsis_id), !is.na(season), !is.na(week)),
#nfl_actualStats_offense_weekly,
nfl_expectedFantasyPoints_weekly %>% filter(!is.na(gsis_id), !is.na(season), !is.na(week))
#nfl_advancedStatsPFR_weekly,
)
playerSeasonWeekPositionListToMerge <- list(
nfl_depthCharts %>% filter(!is.na(gsis_id), !is.na(season), !is.na(week))
)
# Merge data
playerMerged <- playerListToMerge %>%
reduce(
powerjoin::power_full_join,
by = c("gsis_id"),
conflict = coalesce_xy) # where the objects have the same variable name (e.g., position), keep the values from object 1, unless it's NA, in which case use the relevant value from object 2
playerSeasonMerged <- playerSeasonListToMerge %>%
reduce(
powerjoin::power_full_join,
by = c("gsis_id","season"),
conflict = coalesce_xy) # where the objects have the same variable name (e.g., team), keep the values from object 1, unless it's NA, in which case use the relevant value from object 2
playerSeasonWeekMerged <- playerSeasonWeekListToMerge %>%
reduce(
powerjoin::power_full_join,
by = c("gsis_id","season","week"),
conflict = coalesce_xy) # where the objects have the same variable name (e.g., position), keep the values from object 1, unless it's NA, in which case use the relevant value from object 2Identify objects with shared variable names:
[1] "gsis_id" "position"
[3] "position_group" "first_name"
[5] "last_name" "esb_id"
[7] "display_name" "rookie_year"
[9] "college_conference" "current_team_id"
[11] "draft_club" "draft_number"
[13] "draftround" "entry_year"
[15] "football_name" "gsis_it_id"
[17] "headshot" "jersey_number"
[19] "short_name" "smart_id"
[21] "status" "status_description_abbr"
[23] "status_short_description" "uniform_number"
[25] "height" "weight"
[27] "college_name" "birth_date"
[29] "suffix" "years_of_experience"
[31] "pfr_player_name" "team"
[33] "age" Code
seasonalData <- powerjoin::power_full_join(
playerSeasonMerged,
playerMerged %>% select(-age, -years_of_experience, -team, -team_abbr, -team_seq, -current_team_id), # drop variables from id objects that change from year to year (and thus are not necessarily accurate for a given season)
by = "gsis_id",
conflict = coalesce_xy # where the objects have the same variable name (e.g., position), keep the values from object 1, unless it's NA, in which case use the relevant value from object 2
) %>%
filter(!is.na(season)) %>%
select(gsis_id, season, player_display_name, position, team, games, everything()) [1] "gsis_id" "season"
[3] "week" "team"
[5] "jersey_number" "status"
[7] "first_name" "last_name"
[9] "birth_date" "height"
[11] "weight" "college"
[13] "pfr_id" "headshot_url"
[15] "status_description_abbr" "football_name"
[17] "esb_id" "gsis_it_id"
[19] "smart_id" "entry_year"
[21] "rookie_year" "draft_club"
[23] "draft_number" "position" Code
seasonalAndWeeklyData <- powerjoin::power_full_join(
playerSeasonWeekMerged,
seasonalData,
by = c("gsis_id","season"),
conflict = coalesce_xy # where the objects have the same variable name (e.g., position), keep the values from object 1, unless it's NA, in which case use the relevant value from object 2
) %>%
filter(!is.na(week)) %>%
select(gsis_id, season, week, full_name, position, team, everything())19.4.3 Additional Processing
19.4.4 Fill in Missing Data for Static Variables
19.4.5 Create New Data Object for Merging with Later Predictions
19.4.6 Lag Fantasy Points
19.4.7 Subset to Predictor Variables and Outcome Variable
Code
dropVars <- c(
"birth_date", "loaded", "full_name", "player_name", "player_display_name", "display_name", "suffix", "headshot_url", "player", "pos",
"espn_id", "sportradar_id", "yahoo_id", "rotowire_id", "pff_id", "fantasy_data_id", "sleeper_id", "pfr_id",
"pfr_player_id", "cfb_player_id", "pfr_player_name", "esb_id", "gsis_it_id", "smart_id",
"college", "college_name", "team_abbr", "current_team_id", "college_conference", "draft_club", "status_description_abbr",
"status_short_description", "short_name", "headshot", "uniform_number", "jersey_number", "first_name", "last_name",
"football_name", "team")
seasonalData_lag_subset <- seasonalData_lag %>%
dplyr::select(-any_of(dropVars))19.4.8 Separate by Position
Code
seasonalData_lag_subsetQB <- seasonalData_lag_subset %>%
filter(position == "QB") %>%
select(
gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic,
height, weight, rookie_year, draft_number,
fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag,
completions:rushing_2pt_conversions, special_teams_tds, contains(".pass"), contains(".rush"))
seasonalData_lag_subsetRB <- seasonalData_lag_subset %>%
filter(position == "RB") %>%
select(
gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic,
height, weight, rookie_year, draft_number,
fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag,
carries:special_teams_tds, contains(".rush"), contains(".rec"))
seasonalData_lag_subsetWR <- seasonalData_lag_subset %>%
filter(position == "WR") %>%
select(
gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic,
height, weight, rookie_year, draft_number,
fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag,
carries:special_teams_tds, contains(".rush"), contains(".rec"))
seasonalData_lag_subsetTE <- seasonalData_lag_subset %>%
filter(position == "TE") %>%
select(
gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic,
height, weight, rookie_year, draft_number,
fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag,
carries:special_teams_tds, contains(".rush"), contains(".rec"))19.4.9 Split into Test and Training Data
Code
seasonalData_lag_qb_all <- seasonalData_lag_subsetQB
seasonalData_lag_rb_all <- seasonalData_lag_subsetRB
seasonalData_lag_wr_all <- seasonalData_lag_subsetWR
seasonalData_lag_te_all <- seasonalData_lag_subsetTE
set.seed(52242) # for reproducibility (to keep the same train/holdout players)
activeQBs <- unique(seasonalData_lag_qb_all$gsis_id[which(seasonalData_lag_qb_all$season == max(seasonalData_lag_qb_all$season, na.rm = TRUE))])
retiredQBs <- unique(seasonalData_lag_qb_all$gsis_id[which(seasonalData_lag_qb_all$gsis_id %ni% activeQBs)])
numQBs <- length(unique(seasonalData_lag_qb_all$gsis_id))
qbHoldoutIDs <- sample(retiredQBs, size = ceiling(.2 * numQBs)) # holdout 20% of players
activeRBs <- unique(seasonalData_lag_rb_all$gsis_id[which(seasonalData_lag_rb_all$season == max(seasonalData_lag_rb_all$season, na.rm = TRUE))])
retiredRBs <- unique(seasonalData_lag_rb_all$gsis_id[which(seasonalData_lag_rb_all$gsis_id %ni% activeRBs)])
numRBs <- length(unique(seasonalData_lag_rb_all$gsis_id))
rbHoldoutIDs <- sample(retiredRBs, size = ceiling(.2 * numRBs)) # holdout 20% of players
set.seed(52242) # for reproducibility (to keep the same train/holdout players); added here to prevent a downstream error with predict.missRanger() due to missingness; this suggests that an error can arise from including a player in the holdout sample who has missingness in particular variables; would be good to identify which player(s) in the holdout sample evoke that error to identify the kinds of missingness that yield the error
activeWRs <- unique(seasonalData_lag_wr_all$gsis_id[which(seasonalData_lag_wr_all$season == max(seasonalData_lag_wr_all$season, na.rm = TRUE))])
retiredWRs <- unique(seasonalData_lag_wr_all$gsis_id[which(seasonalData_lag_wr_all$gsis_id %ni% activeWRs)])
numWRs <- length(unique(seasonalData_lag_wr_all$gsis_id))
wrHoldoutIDs <- sample(retiredWRs, size = ceiling(.2 * numWRs)) # holdout 20% of players
activeTEs <- unique(seasonalData_lag_te_all$gsis_id[which(seasonalData_lag_te_all$season == max(seasonalData_lag_te_all$season, na.rm = TRUE))])
retiredTEs <- unique(seasonalData_lag_te_all$gsis_id[which(seasonalData_lag_te_all$gsis_id %ni% activeTEs)])
numTEs <- length(unique(seasonalData_lag_te_all$gsis_id))
teHoldoutIDs <- sample(retiredTEs, size = ceiling(.2 * numTEs)) # holdout 20% of players
seasonalData_lag_qb_train <- seasonalData_lag_qb_all %>%
filter(gsis_id %ni% qbHoldoutIDs)
seasonalData_lag_qb_test <- seasonalData_lag_qb_all %>%
filter(gsis_id %in% qbHoldoutIDs)
seasonalData_lag_rb_train <- seasonalData_lag_rb_all %>%
filter(gsis_id %ni% rbHoldoutIDs)
seasonalData_lag_rb_test <- seasonalData_lag_rb_all %>%
filter(gsis_id %in% rbHoldoutIDs)
seasonalData_lag_wr_train <- seasonalData_lag_wr_all %>%
filter(gsis_id %ni% wrHoldoutIDs)
seasonalData_lag_wr_test <- seasonalData_lag_wr_all %>%
filter(gsis_id %in% wrHoldoutIDs)
seasonalData_lag_te_train <- seasonalData_lag_te_all %>%
filter(gsis_id %ni% teHoldoutIDs)
seasonalData_lag_te_test <- seasonalData_lag_te_all %>%
filter(gsis_id %in% teHoldoutIDs)19.4.10 Impute the Missing Data
Many of the machine learning approaches described in this chapter require no missing observations in order for a case to be included in the analysis. In this section, we demonstrate one approach to imputing missing data. Here is a vignette demonstrating how to impute missing data using missForest(): https://rpubs.com/lmorgan95/MissForest (archived at: https://perma.cc/6GB4-2E22). Below, we impute the training data (and all data) separately by position. We then use the imputed training data to make out-of-sample predictions to fill in the missing data for the testing data. We do not want to impute the training and testing data together so that we can keep them separate for the purposes of cross-validation. However, we impute all data (training and test data together) for purposes of making out-of-sample predictions from the machine learning models to predict players’ performance next season (when actuals are not yet available for evaluating their accuracy). To impute data, we use the missRanger package (Mayer, 2024).
Note 19.1: Impute missing data for machine learning
Note: the following code takes a while to run.
Code
Variables to impute: fantasy_points, fantasy_points_ppr, special_teams_tds, passing_epa, pacr, rushing_epa, fantasyPoints_lag, passing_cpoe, rookie_year, draft_number, gs, pass_attempts.pass, throwaways.pass, spikes.pass, drops.pass, bad_throws.pass, times_blitzed.pass, times_hurried.pass, times_hit.pass, times_pressured.pass, batted_balls.pass, on_tgt_throws.pass, rpo_plays.pass, rpo_yards.pass, rpo_pass_att.pass, rpo_pass_yards.pass, rpo_rush_att.pass, rpo_rush_yards.pass, pa_pass_att.pass, pa_pass_yards.pass, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, drop_pct.pass, bad_throw_pct.pass, on_tgt_pct.pass, pressure_pct.pass, ybc_att.rush, yac_att.rush, pocket_time.pass
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, completions, attempts, passing_yards, passing_tds, passing_interceptions, sacks_suffered, sack_yards_lost, sack_fumbles, sack_fumbles_lost, passing_air_yards, passing_yards_after_catch, passing_first_downs, passing_epa, passing_cpoe, passing_2pt_conversions, pacr, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, special_teams_tds, pocket_time.pass, pass_attempts.pass, throwaways.pass, spikes.pass, drops.pass, bad_throws.pass, times_blitzed.pass, times_hurried.pass, times_hit.pass, times_pressured.pass, batted_balls.pass, on_tgt_throws.pass, rpo_plays.pass, rpo_yards.pass, rpo_pass_att.pass, rpo_pass_yards.pass, rpo_rush_att.pass, rpo_rush_yards.pass, pa_pass_att.pass, pa_pass_yards.pass, drop_pct.pass, bad_throw_pct.pass, on_tgt_pct.pass, pressure_pct.pass, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush
fntsy_ fnts__ spcl__ pssng_p pacr rshng_ fntsP_ pssng_c rok_yr drft_n gs pss_t. thrww. spks.p drps.p bd_th. tms_b. tms_hr. tms_ht. tms_p. bttd_. on_tgt_t. rp_pl. rp_yr. rp_pss_t. rp_pss_y. rp_rsh_t. rp_rsh_y. p_pss_t. p_pss_y. att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_t. att_b. drp_p. bd_t_. on_tgt_p. prss_. ybc_t. yc_tt. pckt_.
iter 1: 0.0054 0.0024 0.7924 0.1919 0.7612 0.3628 0.4789 0.4133 0.0224 0.5216 0.0271 0.0134 0.3024 0.7659 0.1304 0.0541 0.0758 0.1759 0.1820 0.0370 0.3238 0.0291 0.2952 0.1812 0.0885 0.0867 0.2627 0.2563 0.1093 0.0902 0.0580 0.0645 0.1732 0.0524 0.0578 0.1795 0.3524 0.3428 0.7447 0.5158 0.0824 0.6803 0.3529 0.5758 0.8111
iter 2: 0.0044 0.0048 0.8304 0.2002 0.7926 0.3736 0.4801 0.4289 0.0488 0.6139 0.0188 0.0090 0.2883 0.7481 0.0764 0.0385 0.0718 0.1231 0.1329 0.0337 0.2760 0.0113 0.0548 0.0814 0.0765 0.0990 0.1989 0.2841 0.0707 0.0952 0.0396 0.0386 0.1606 0.0492 0.0525 0.1220 0.2541 0.3556 0.7468 0.4937 0.0827 0.6610 0.3465 0.5796 0.8134
iter 3: 0.0049 0.0046 0.8690 0.1986 0.7810 0.3641 0.4774 0.4360 0.0528 0.6123 0.0188 0.0088 0.2867 0.7538 0.0767 0.0393 0.0734 0.1261 0.1374 0.0343 0.2741 0.0119 0.0524 0.0816 0.0748 0.1008 0.2184 0.2811 0.0691 0.0926 0.0389 0.0413 0.1640 0.0511 0.0585 0.1255 0.2510 0.3609 0.7477 0.5108 0.0858 0.6426 0.3588 0.5734 0.8300 missRanger object. Extract imputed data via $data
- best iteration: 2
- best average OOB imputation error: 0.2524825 Code
data_all_qb <- seasonalData_lag_qb_all_imp$data
data_all_qb$fantasyPointsMC_lag <- scale(data_all_qb$fantasyPoints_lag, scale = FALSE) # mean-centered
data_all_qb_matrix <- data_all_qb %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
newData_qb <- data_all_qb %>%
filter(season == max(season, na.rm = TRUE)) %>%
select(-fantasyPoints_lag, -fantasyPointsMC_lag)
newData_qb_matrix <- data_all_qb_matrix[
data_all_qb_matrix[, "season"] == max(data_all_qb_matrix[, "season"], na.rm = TRUE), # keep only rows with the most recent season
, # all columns
drop = FALSE]
dropCol_qb <- which(colnames(newData_qb_matrix) %in% c("fantasyPoints_lag","fantasyPointsMC_lag"))
newData_qb_matrix <- newData_qb_matrix[, -dropCol_qb, drop = FALSE]
seasonalData_lag_qb_train_imp <- missRanger::missRanger(
seasonalData_lag_qb_train,
pmm.k = 5,
verbose = 2,
seed = 52242,
keep_forests = TRUE)
Variables to impute: fantasy_points, fantasy_points_ppr, special_teams_tds, passing_epa, pacr, rushing_epa, fantasyPoints_lag, passing_cpoe, rookie_year, draft_number, gs, pass_attempts.pass, throwaways.pass, spikes.pass, drops.pass, bad_throws.pass, times_blitzed.pass, times_hurried.pass, times_hit.pass, times_pressured.pass, batted_balls.pass, on_tgt_throws.pass, rpo_plays.pass, rpo_yards.pass, rpo_pass_att.pass, rpo_pass_yards.pass, rpo_rush_att.pass, rpo_rush_yards.pass, pa_pass_att.pass, pa_pass_yards.pass, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, drop_pct.pass, bad_throw_pct.pass, on_tgt_pct.pass, pressure_pct.pass, ybc_att.rush, yac_att.rush, pocket_time.pass
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, completions, attempts, passing_yards, passing_tds, passing_interceptions, sacks_suffered, sack_yards_lost, sack_fumbles, sack_fumbles_lost, passing_air_yards, passing_yards_after_catch, passing_first_downs, passing_epa, passing_cpoe, passing_2pt_conversions, pacr, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, special_teams_tds, pocket_time.pass, pass_attempts.pass, throwaways.pass, spikes.pass, drops.pass, bad_throws.pass, times_blitzed.pass, times_hurried.pass, times_hit.pass, times_pressured.pass, batted_balls.pass, on_tgt_throws.pass, rpo_plays.pass, rpo_yards.pass, rpo_pass_att.pass, rpo_pass_yards.pass, rpo_rush_att.pass, rpo_rush_yards.pass, pa_pass_att.pass, pa_pass_yards.pass, drop_pct.pass, bad_throw_pct.pass, on_tgt_pct.pass, pressure_pct.pass, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush
fntsy_ fnts__ spcl__ pssng_p pacr rshng_ fntsP_ pssng_c rok_yr drft_n gs pss_t. thrww. spks.p drps.p bd_th. tms_b. tms_hr. tms_ht. tms_p. bttd_. on_tgt_t. rp_pl. rp_yr. rp_pss_t. rp_pss_y. rp_rsh_t. rp_rsh_y. p_pss_t. p_pss_y. att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_t. att_b. drp_p. bd_t_. on_tgt_p. prss_. ybc_t. yc_tt. pckt_.
iter 1: 0.0061 0.0028 0.8162 0.1897 0.5083 0.3633 0.4726 0.4456 0.0242 0.4723 0.0283 0.0141 0.2939 0.7728 0.1343 0.0558 0.0744 0.1757 0.1818 0.0381 0.3288 0.0351 0.2921 0.1846 0.0860 0.0894 0.2737 0.2661 0.1127 0.0900 0.0586 0.0644 0.1800 0.0574 0.0639 0.1792 0.3570 0.3486 0.7646 0.5313 0.0868 0.7084 0.3533 0.5933 0.8466
iter 2: 0.0052 0.0052 0.8304 0.1937 0.5621 0.3715 0.4614 0.4586 0.0505 0.5647 0.0192 0.0092 0.2953 0.7530 0.0800 0.0393 0.0725 0.1170 0.1355 0.0343 0.2771 0.0121 0.0555 0.0731 0.0713 0.0979 0.2073 0.2943 0.0698 0.0911 0.0416 0.0399 0.1683 0.0527 0.0577 0.1262 0.2474 0.3582 0.7719 0.5165 0.0900 0.6862 0.3642 0.5926 0.8400
iter 3: 0.0053 0.0051 0.8261 0.2008 0.5551 0.3571 0.4727 0.4410 0.0551 0.5658 0.0188 0.0092 0.2859 0.7460 0.0807 0.0402 0.0739 0.1202 0.1393 0.0351 0.2808 0.0114 0.0595 0.0705 0.0775 0.1051 0.2163 0.2935 0.0718 0.0921 0.0426 0.0400 0.1719 0.0535 0.0534 0.1225 0.2498 0.3484 0.7502 0.5100 0.0884 0.6609 0.3672 0.5852 0.8440
iter 4: 0.0054 0.0051 0.6928 0.1979 0.5598 0.3732 0.4771 0.4349 0.0506 0.5691 0.0189 0.0085 0.2891 0.7456 0.0785 0.0395 0.0737 0.1210 0.1353 0.0335 0.2836 0.0117 0.0566 0.0778 0.0743 0.1055 0.2131 0.2964 0.0697 0.0912 0.0396 0.0395 0.1611 0.0531 0.0597 0.1258 0.2600 0.3560 0.8062 0.5032 0.0973 0.6739 0.3698 0.5875 0.8485
iter 5: 0.0052 0.0055 0.8355 0.1965 0.5664 0.3710 0.4743 0.4604 0.0520 0.5598 0.0193 0.0091 0.2852 0.7474 0.0800 0.0405 0.0722 0.1213 0.1366 0.0344 0.2788 0.0118 0.0555 0.0756 0.0746 0.0986 0.2190 0.2765 0.0695 0.0932 0.0390 0.0425 0.1650 0.0509 0.0576 0.1305 0.2556 0.3509 0.7738 0.5051 0.0969 0.6902 0.3640 0.6007 0.8326 missRanger object. Extract imputed data via $data
- best iteration: 4
- best average OOB imputation error: 0.2482278 Code
data_train_qb <- seasonalData_lag_qb_train_imp$data
data_train_qb$fantasyPointsMC_lag <- scale(data_train_qb$fantasyPoints_lag, scale = FALSE) # mean-centered
data_train_qb_matrix <- data_train_qb %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
seasonalData_lag_qb_test_imp <- predict(
object = seasonalData_lag_qb_train_imp,
newdata = seasonalData_lag_qb_test,
seed = 52242)
data_test_qb <- seasonalData_lag_qb_test_imp
data_test_qb_matrix <- data_test_qb %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()Code
Variables to impute: games, ageCentered20, ageCentered20Quadratic, fantasy_points, fantasy_points_ppr, fantasyPoints, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_2pt_conversions, special_teams_tds, years_of_experience, rushing_epa, air_yards_share, receiving_epa, racr, target_share, wopr, fantasyPoints_lag, rookie_year, draft_number, gs, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, ybc_att.rush, yac_att.rush, adot.rec, rat.rec, drop_percent.rec, rec_br.rec, ybc_r.rec, yac_r.rec
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_epa, receiving_2pt_conversions, racr, target_share, air_yards_share, wopr, special_teams_tds, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, ybc_r.rec, yac_r.rec, adot.rec, rat.rec, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, drop_percent.rec, rec_br.rec
games agCn20 agC20Q fntsy_ fnts__ fntsyP carris rshng_y rshng_t rshng_f rshng_fm_ rshng_fr_ rsh_2_ rcptns targts rcvng_y rcvng_t rcvng_f rcvng_fm_ rcvng_r_ rcv___ rcvng_fr_ rcv_2_ spcl__ yrs_f_ rshng_p ar_yr_ rcvng_p racr trgt_s wopr fntsP_ rok_yr drft_n gs att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_tkl.rs att_b. tgt.rc rec.rc yds.rc td.rec x1d.rc ybc.rc yac.rc brk_tkl.rc drp.rc int.rc ybc_t. yc_tt. adt.rc rat.rc drp_p. rc_br. ybc_r. yc_r.r
iter 1: 0.8865 0.0057 0.0031 0.4544 0.0178 0.0032 0.0745 0.0233 0.1462 0.4895 0.2594 0.0295 0.9849 0.0690 0.0666 0.0534 0.4327 0.8626 0.4824 0.6841 0.0322 0.0614 1.0171 0.8263 0.1817 0.4512 0.3321 0.3894 0.5211 0.4549 0.1817 0.5440 0.0197 0.5999 0.1700 0.0244 0.0222 0.0792 0.0297 0.0527 0.0520 0.2134 0.3431 0.0252 0.0180 0.0257 0.1634 0.0437 0.3108 0.0217 0.3925 0.4610 0.6941 0.4880 0.5402 0.2670 0.2026 0.3482 0.1596 0.2698 0.3637
iter 2: 0.2755 0.0162 0.0207 0.0063 0.0037 0.0044 0.0161 0.0148 0.0912 0.2524 0.2898 0.0248 0.9832 0.0273 0.0444 0.0233 0.2065 0.4600 0.4891 0.1285 0.0332 0.0457 1.0175 0.8566 0.1824 0.4212 0.2329 0.3099 0.5605 0.2569 0.1742 0.5373 0.0424 0.6377 0.1653 0.0167 0.0123 0.0859 0.0302 0.0367 0.0349 0.1030 0.3689 0.0144 0.0159 0.0195 0.1403 0.0434 0.1549 0.0190 0.3840 0.1050 0.5453 0.4882 0.5616 0.2472 0.1953 0.1525 0.1687 0.2595 0.3619
iter 3: 0.2744 0.0163 0.0231 0.0062 0.0038 0.0047 0.0152 0.0137 0.0980 0.2601 0.2906 0.0244 0.9800 0.0265 0.0347 0.0231 0.2101 0.4638 0.4954 0.1284 0.0283 0.0458 1.0114 0.8731 0.1818 0.4117 0.2278 0.3037 0.5699 0.2052 0.1800 0.5389 0.0400 0.6423 0.1624 0.0166 0.0124 0.0893 0.0306 0.0374 0.0356 0.1074 0.3628 0.0144 0.0163 0.0187 0.1390 0.0463 0.1583 0.0190 0.3882 0.1062 0.5642 0.4796 0.5570 0.2380 0.1935 0.1586 0.1588 0.2625 0.3648
iter 4: 0.2776 0.0169 0.0220 0.0063 0.0038 0.0045 0.0151 0.0138 0.0979 0.2584 0.2846 0.0243 0.9782 0.0263 0.0281 0.0221 0.1968 0.4594 0.4817 0.1267 0.0290 0.0462 1.0104 0.8614 0.1854 0.4216 0.2333 0.3004 0.5467 0.1917 0.1815 0.5353 0.0443 0.6503 0.1657 0.0166 0.0121 0.0905 0.0313 0.0378 0.0357 0.1041 0.3437 0.0155 0.0159 0.0185 0.1405 0.0441 0.1613 0.0196 0.3816 0.1117 0.5682 0.5011 0.5585 0.2421 0.1975 0.1520 0.1770 0.2650 0.3647
iter 5: 0.2752 0.0163 0.0226 0.0063 0.0038 0.0045 0.0158 0.0138 0.1015 0.2614 0.2857 0.0242 0.9740 0.0250 0.0303 0.0218 0.2004 0.4607 0.4810 0.1167 0.0285 0.0449 1.0077 0.8658 0.1835 0.4182 0.2170 0.2995 0.5690 0.2010 0.1794 0.5375 0.0385 0.6487 0.1652 0.0166 0.0124 0.0878 0.0306 0.0368 0.0353 0.1069 0.3539 0.0154 0.0159 0.0193 0.1409 0.0447 0.1598 0.0205 0.3873 0.1062 0.5583 0.4895 0.5501 0.2418 0.1979 0.1713 0.1726 0.2625 0.3596
iter 6: 0.2760 0.0158 0.0223 0.0063 0.0037 0.0046 0.0150 0.0144 0.0982 0.2568 0.2816 0.0238 0.9810 0.0253 0.0273 0.0223 0.2141 0.4606 0.4881 0.1386 0.0300 0.0457 1.0174 0.8605 0.1821 0.4188 0.2263 0.2985 0.5497 0.1779 0.1536 0.5388 0.0389 0.6422 0.1668 0.0162 0.0119 0.0897 0.0305 0.0376 0.0356 0.1066 0.3529 0.0149 0.0159 0.0196 0.1446 0.0450 0.1585 0.0197 0.3857 0.1001 0.5607 0.4948 0.5478 0.2487 0.1945 0.1438 0.1543 0.2568 0.3612
iter 7: 0.2748 0.0158 0.0212 0.0064 0.0039 0.0047 0.0149 0.0141 0.0986 0.2611 0.2877 0.0241 0.9755 0.0253 0.0310 0.0223 0.2163 0.4553 0.4885 0.1335 0.0293 0.0456 1.0096 0.8575 0.1821 0.4236 0.2203 0.2998 0.5510 0.2107 0.1797 0.5354 0.0416 0.6395 0.1646 0.0166 0.0117 0.0895 0.0310 0.0371 0.0361 0.1073 0.3547 0.0154 0.0156 0.0193 0.1410 0.0449 0.1628 0.0201 0.3892 0.1076 0.5609 0.4946 0.5643 0.2392 0.1899 0.1540 0.1423 0.2667 0.3603 missRanger object. Extract imputed data via $data
- best iteration: 6
- best average OOB imputation error: 0.2175486 Code
data_all_rb <- seasonalData_lag_rb_all_imp$data
data_all_rb$fantasyPointsMC_lag <- scale(data_all_rb$fantasyPoints_lag, scale = FALSE) # mean-centered
data_all_rb_matrix <- data_all_rb %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
newData_rb <- data_all_rb %>%
filter(season == max(season, na.rm = TRUE)) %>%
select(-fantasyPoints_lag, -fantasyPointsMC_lag)
newData_rb_matrix <- data_all_rb_matrix[
data_all_rb_matrix[, "season"] == max(data_all_rb_matrix[, "season"], na.rm = TRUE), # keep only rows with the most recent season
, # all columns
drop = FALSE]
dropCol_rb <- which(colnames(newData_rb_matrix) %in% c("fantasyPoints_lag","fantasyPointsMC_lag"))
newData_rb_matrix <- newData_rb_matrix[, -dropCol_rb, drop = FALSE]
seasonalData_lag_rb_train_imp <- missRanger::missRanger(
seasonalData_lag_rb_train,
pmm.k = 5,
verbose = 2,
seed = 52242,
keep_forests = TRUE)
Variables to impute: games, ageCentered20, ageCentered20Quadratic, fantasy_points, fantasy_points_ppr, fantasyPoints, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_2pt_conversions, special_teams_tds, years_of_experience, rushing_epa, air_yards_share, receiving_epa, racr, target_share, wopr, fantasyPoints_lag, rookie_year, draft_number, gs, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, ybc_att.rush, yac_att.rush, adot.rec, rat.rec, drop_percent.rec, rec_br.rec, ybc_r.rec, yac_r.rec
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_epa, receiving_2pt_conversions, racr, target_share, air_yards_share, wopr, special_teams_tds, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, ybc_r.rec, yac_r.rec, adot.rec, rat.rec, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, drop_percent.rec, rec_br.rec
games agCn20 agC20Q fntsy_ fnts__ fntsyP carris rshng_y rshng_t rshng_f rshng_fm_ rshng_fr_ rsh_2_ rcptns targts rcvng_y rcvng_t rcvng_f rcvng_fm_ rcvng_r_ rcv___ rcvng_fr_ rcv_2_ spcl__ yrs_f_ rshng_p ar_yr_ rcvng_p racr trgt_s wopr fntsP_ rok_yr drft_n gs att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_tkl.rs att_b. tgt.rc rec.rc yds.rc td.rec x1d.rc ybc.rc yac.rc brk_tkl.rc drp.rc int.rc ybc_t. yc_tt. adt.rc rat.rc drp_p. rc_br. ybc_r. yc_r.r
iter 1: 0.8759 0.0072 0.0036 0.4578 0.0178 0.0035 0.0736 0.0229 0.1524 0.4776 0.2679 0.0288 0.9965 0.0749 0.0744 0.0553 0.4578 0.8604 0.4998 0.6821 0.0360 0.0639 1.0042 0.8380 0.1806 0.4662 0.3419 0.3961 0.5595 0.4715 0.1968 0.5338 0.0246 0.5882 0.1726 0.0265 0.0235 0.0849 0.0311 0.0550 0.0521 0.2131 0.3689 0.0281 0.0197 0.0286 0.1742 0.0463 0.3114 0.0229 0.3942 0.4806 0.7239 0.5199 0.5631 0.2865 0.2195 0.3630 0.2052 0.2596 0.4091
iter 2: 0.2745 0.0177 0.0266 0.0067 0.0041 0.0049 0.0169 0.0154 0.1017 0.2590 0.2956 0.0237 0.9814 0.0286 0.0522 0.0240 0.2187 0.4582 0.4919 0.1541 0.0362 0.0475 1.0075 0.8811 0.1822 0.4481 0.2377 0.3184 0.6116 0.2628 0.2007 0.5254 0.0473 0.6411 0.1653 0.0179 0.0132 0.0940 0.0325 0.0392 0.0375 0.1057 0.3678 0.0161 0.0169 0.0196 0.1510 0.0484 0.1521 0.0202 0.3941 0.1035 0.5567 0.5120 0.5666 0.2466 0.2087 0.1733 0.1699 0.2524 0.4066
iter 3: 0.2766 0.0190 0.0273 0.0067 0.0041 0.0048 0.0159 0.0149 0.0971 0.2615 0.2952 0.0245 0.9668 0.0278 0.0409 0.0240 0.2180 0.4648 0.4931 0.1319 0.0350 0.0495 1.0128 0.8907 0.1820 0.4366 0.2459 0.3124 0.6236 0.2555 0.2114 0.5276 0.0438 0.6314 0.1658 0.0175 0.0122 0.0899 0.0319 0.0386 0.0386 0.1100 0.3783 0.0155 0.0165 0.0194 0.1477 0.0474 0.1499 0.0194 0.3929 0.1121 0.5761 0.5245 0.5651 0.2490 0.2103 0.1767 0.1817 0.2658 0.4106 missRanger object. Extract imputed data via $data
- best iteration: 2
- best average OOB imputation error: 0.226086 Code
data_train_rb <- seasonalData_lag_rb_train_imp$data
data_train_rb$fantasyPointsMC_lag <- scale(data_train_rb$fantasyPoints_lag, scale = FALSE) # mean-centered
data_train_rb_matrix <- data_train_rb %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
seasonalData_lag_rb_test_imp <- predict(
object = seasonalData_lag_rb_train_imp,
newdata = seasonalData_lag_rb_test,
seed = 52242)
data_test_rb <- seasonalData_lag_rb_test_imp
data_test_rb_matrix <- data_test_rb %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()Code
Variables to impute: fantasy_points, fantasy_points_ppr, special_teams_tds, years_of_experience, receiving_epa, racr, air_yards_share, target_share, wopr, fantasyPoints_lag, rookie_year, rushing_epa, draft_number, gs, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, adot.rec, rat.rec, drop_percent.rec, rec_br.rec, ybc_r.rec, yac_r.rec, ybc_att.rush, yac_att.rush
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_epa, receiving_2pt_conversions, racr, target_share, air_yards_share, wopr, special_teams_tds, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, ybc_r.rec, yac_r.rec, adot.rec, rat.rec, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, drop_percent.rec, rec_br.rec
fntsy_ fnts__ spcl__ yrs_f_ rcvng_ racr ar_yr_ trgt_s wopr fntsP_ rok_yr rshng_ drft_n gs att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_tkl.rs att_b. tgt.rc rec.rc yds.rc td.rec x1d.rc ybc.rc yac.rc brk_tkl.rc drp.rc int.rc adt.rc rat.rc drp_p. rc_br. ybc_r. yc_r.r ybc_t. yc_tt.
iter 1: 0.0061 0.0010 0.7104 0.1566 0.1040 0.8131 0.1013 0.1722 0.0402 0.4890 0.0150 0.3811 0.6654 0.1459 0.1184 0.0898 0.2353 0.1234 0.0966 0.2670 0.6383 0.3084 0.0198 0.0136 0.0151 0.0671 0.0135 0.0268 0.0442 0.4465 0.4320 0.4674 0.2961 0.1410 0.3819 0.1840 0.2251 0.3929 0.2568 0.4760
iter 2: 0.0058 0.0019 0.7826 0.1601 0.0835 0.7518 0.0607 0.0930 0.0452 0.4939 0.0296 0.3301 0.6843 0.1440 0.0851 0.0600 0.2638 0.1161 0.0708 0.1804 0.3103 0.3223 0.0109 0.0108 0.0096 0.0719 0.0139 0.0200 0.0318 0.4476 0.0778 0.3692 0.2401 0.1448 0.1629 0.1601 0.2261 0.3793 0.2536 0.4775
iter 3: 0.0061 0.0019 0.7857 0.1593 0.0829 0.7421 0.0580 0.0986 0.0481 0.4946 0.0318 0.3334 0.6890 0.1430 0.0823 0.0604 0.2595 0.1177 0.0728 0.1802 0.3077 0.3194 0.0109 0.0114 0.0095 0.0724 0.0133 0.0199 0.0312 0.4411 0.0767 0.3687 0.2369 0.1455 0.1530 0.1660 0.2169 0.3878 0.2466 0.4716
iter 4: 0.0060 0.0018 0.7874 0.1604 0.0832 0.7394 0.0591 0.0940 0.0479 0.4926 0.0301 0.3317 0.6896 0.1434 0.0863 0.0601 0.2562 0.1227 0.0711 0.1900 0.3089 0.3194 0.0105 0.0112 0.0095 0.0707 0.0140 0.0202 0.0318 0.4447 0.0784 0.3674 0.2339 0.1423 0.1592 0.1700 0.2254 0.3886 0.2552 0.4662 missRanger object. Extract imputed data via $data
- best iteration: 3
- best average OOB imputation error: 0.203846 Code
data_all_wr <- seasonalData_lag_wr_all_imp$data
data_all_wr$fantasyPointsMC_lag <- scale(data_all_wr$fantasyPoints_lag, scale = FALSE) # mean-centered
data_all_wr_matrix <- data_all_wr %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
newData_wr <- data_all_wr %>%
filter(season == max(season, na.rm = TRUE)) %>%
select(-fantasyPoints_lag, -fantasyPointsMC_lag)
newData_wr_matrix <- data_all_wr_matrix[
data_all_wr_matrix[, "season"] == max(data_all_wr_matrix[, "season"], na.rm = TRUE), # keep only rows with the most recent season
, # all columns
drop = FALSE]
dropCol_wr <- which(colnames(newData_wr_matrix) %in% c("fantasyPoints_lag","fantasyPointsMC_lag"))
newData_wr_matrix <- newData_wr_matrix[, -dropCol_wr, drop = FALSE]
seasonalData_lag_wr_train_imp <- missRanger::missRanger(
seasonalData_lag_wr_train,
pmm.k = 5,
verbose = 2,
seed = 52242,
keep_forests = TRUE)
Variables to impute: fantasy_points, fantasy_points_ppr, special_teams_tds, years_of_experience, receiving_epa, racr, air_yards_share, target_share, wopr, fantasyPoints_lag, rookie_year, rushing_epa, draft_number, gs, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, adot.rec, rat.rec, drop_percent.rec, rec_br.rec, ybc_r.rec, yac_r.rec, ybc_att.rush, yac_att.rush
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_epa, receiving_2pt_conversions, racr, target_share, air_yards_share, wopr, special_teams_tds, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, ybc_r.rec, yac_r.rec, adot.rec, rat.rec, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, drop_percent.rec, rec_br.rec
fntsy_ fnts__ spcl__ yrs_f_ rcvng_ racr ar_yr_ trgt_s wopr fntsP_ rok_yr rshng_ drft_n gs att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_tkl.rs att_b. tgt.rc rec.rc yds.rc td.rec x1d.rc ybc.rc yac.rc brk_tkl.rc drp.rc int.rc adt.rc rat.rc drp_p. rc_br. ybc_r. yc_r.r ybc_t. yc_tt.
iter 1: 0.0064 0.0010 0.7029 0.1611 0.1089 0.8443 0.1021 0.1643 0.0427 0.4935 0.0173 0.3461 0.6788 0.1427 0.1364 0.0993 0.2403 0.1243 0.0979 0.2745 0.6190 0.3171 0.0201 0.0147 0.0159 0.0734 0.0140 0.0280 0.0454 0.4502 0.4439 0.4733 0.3088 0.1641 0.4547 0.2192 0.2439 0.4227 0.2921 0.5068
iter 2: 0.0063 0.0020 0.7835 0.1630 0.0901 0.8044 0.0674 0.0936 0.0479 0.4930 0.0331 0.3235 0.7090 0.1417 0.0896 0.0659 0.2659 0.1273 0.0752 0.1920 0.3068 0.3225 0.0112 0.0116 0.0101 0.0753 0.0141 0.0210 0.0333 0.4431 0.0809 0.3676 0.2571 0.1617 0.1735 0.1797 0.2441 0.3996 0.2911 0.4923
iter 3: 0.0063 0.0020 0.7710 0.1639 0.0881 0.7954 0.0646 0.0982 0.0515 0.4956 0.0338 0.3200 0.7088 0.1413 0.0900 0.0640 0.2565 0.1250 0.0735 0.1989 0.3082 0.3280 0.0114 0.0119 0.0096 0.0763 0.0141 0.0216 0.0326 0.4388 0.0807 0.3703 0.2582 0.1623 0.1657 0.2018 0.2375 0.4016 0.2838 0.4794
iter 4: 0.0062 0.0020 0.7792 0.1625 0.0877 0.8043 0.0632 0.0919 0.0477 0.4963 0.0341 0.3239 0.7038 0.1420 0.0950 0.0653 0.2664 0.1309 0.0767 0.2025 0.2933 0.3076 0.0109 0.0119 0.0097 0.0745 0.0143 0.0217 0.0326 0.4432 0.0804 0.3688 0.2584 0.1605 0.1931 0.2013 0.2378 0.4119 0.2824 0.4860 missRanger object. Extract imputed data via $data
- best iteration: 3
- best average OOB imputation error: 0.2110456 Code
data_train_wr <- seasonalData_lag_wr_train_imp$data
data_train_wr$fantasyPointsMC_lag <- scale(data_train_wr$fantasyPoints_lag, scale = FALSE) # mean-centered
data_train_wr_matrix <- data_train_wr %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
seasonalData_lag_wr_test_imp <- predict(
object = seasonalData_lag_wr_train_imp,
newdata = seasonalData_lag_wr_test,
seed = 52242)
data_test_wr <- seasonalData_lag_wr_test_imp
data_test_wr_matrix <- data_test_wr %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()Code
Variables to impute: games, ageCentered20, ageCentered20Quadratic, fantasy_points, fantasy_points_ppr, fantasyPoints, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_2pt_conversions, special_teams_tds, years_of_experience, receiving_epa, racr, air_yards_share, target_share, wopr, fantasyPoints_lag, rookie_year, draft_number, gs, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, adot.rec, rat.rec, drop_percent.rec, rec_br.rec, ybc_r.rec, yac_r.rec, rushing_epa, ybc_att.rush, yac_att.rush
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_epa, receiving_2pt_conversions, racr, target_share, air_yards_share, wopr, special_teams_tds, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, ybc_r.rec, yac_r.rec, adot.rec, rat.rec, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, drop_percent.rec, rec_br.rec
games agCn20 agC20Q fntsy_ fnts__ fntsyP carris rshng_y rshng_t rshng_f rshng_fm_ rshng_fr_ rsh_2_ rcptns targts rcvng_y rcvng_t rcvng_f rcvng_fm_ rcvng_r_ rcv___ rcvng_fr_ rcv_2_ spcl__ yrs_f_ rcvng_p racr ar_yr_ trgt_s wopr fntsP_ rok_yr drft_n gs att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_tkl.rs att_b. tgt.rc rec.rc yds.rc td.rec x1d.rc ybc.rc yac.rc brk_tkl.rc drp.rc int.rc adt.rc rat.rc drp_p. rc_br. ybc_r. yc_r.r rshng_p ybc_t. yc_tt.
iter 1: 0.8157 0.0061 0.0030 0.3406 0.0194 0.0039 0.5253 0.2259 0.2452 0.7083 0.6874 0.0802 1.1303 0.0281 0.0558 0.0255 0.0845 0.8134 0.4317 0.0655 0.0784 0.0253 0.9716 1.0271 0.1530 0.1689 0.6899 0.1092 0.4432 0.1004 0.4764 0.0180 0.6054 0.3846 0.0762 0.0832 0.1564 0.0618 0.0704 0.2123 0.3921 0.6733 0.0290 0.0207 0.0226 0.1012 0.0212 0.0420 0.0603 0.4332 0.4640 0.4996 0.2804 0.1667 0.3542 0.1652 0.2843 0.3948 0.3270 0.6620 0.7439
iter 2: 0.1712 0.0175 0.0256 0.0106 0.0037 0.0055 0.1140 0.1113 0.0990 0.5369 0.7422 0.0852 1.1286 0.0193 0.0200 0.0128 0.0862 0.4248 0.4659 0.0206 0.0529 0.0217 0.9711 1.0114 0.1561 0.1397 0.6715 0.0766 0.1819 0.1085 0.4649 0.0366 0.6346 0.3880 0.0722 0.0728 0.1592 0.0680 0.0759 0.2034 0.3651 0.6811 0.0164 0.0158 0.0161 0.1080 0.0211 0.0327 0.0475 0.4342 0.1149 0.4173 0.2589 0.1742 0.1467 0.1531 0.2941 0.3851 0.3357 0.6846 0.7397
iter 3: 0.1689 0.0170 0.0261 0.0114 0.0040 0.0056 0.1190 0.1155 0.0978 0.6088 0.7899 0.0945 1.1731 0.0195 0.0203 0.0132 0.0964 0.4270 0.4608 0.0202 0.0525 0.0214 0.9694 1.0265 0.1560 0.1380 0.6453 0.0751 0.1794 0.1204 0.4642 0.0364 0.6369 0.3853 0.0779 0.0786 0.1497 0.0569 0.0932 0.2027 0.4003 0.6633 0.0171 0.0164 0.0167 0.1049 0.0220 0.0335 0.0466 0.4371 0.1141 0.4304 0.2665 0.1775 0.1464 0.1537 0.2916 0.3720 0.3137 0.6640 0.7770 missRanger object. Extract imputed data via $data
- best iteration: 2
- best average OOB imputation error: 0.2477098 Code
data_all_te <- seasonalData_lag_te_all_imp$data
data_all_te$fantasyPointsMC_lag <- scale(data_all_te$fantasyPoints_lag, scale = FALSE) # mean-centered
data_all_te_matrix <- data_all_te %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
newData_te <- data_all_te %>%
filter(season == max(season, na.rm = TRUE)) %>%
select(-fantasyPoints_lag, -fantasyPointsMC_lag)
newData_te_matrix <- data_all_te_matrix[
data_all_te_matrix[, "season"] == max(data_all_te_matrix[, "season"], na.rm = TRUE), # keep only rows with the most recent season
, # all columns
drop = FALSE]
dropCol_te <- which(colnames(newData_te_matrix) %in% c("fantasyPoints_lag","fantasyPointsMC_lag"))
newData_te_matrix <- newData_te_matrix[, -dropCol_te, drop = FALSE]
seasonalData_lag_te_train_imp <- missRanger::missRanger(
seasonalData_lag_te_train,
pmm.k = 5,
verbose = 2,
seed = 52242,
keep_forests = TRUE)
Variables to impute: games, years_of_experience, ageCentered20, ageCentered20Quadratic, fantasy_points, fantasy_points_ppr, fantasyPoints, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_2pt_conversions, special_teams_tds, receiving_epa, racr, air_yards_share, target_share, wopr, fantasyPoints_lag, rookie_year, draft_number, gs, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, adot.rec, rat.rec, drop_percent.rec, rec_br.rec, ybc_r.rec, yac_r.rec, rushing_epa, ybc_att.rush, yac_att.rush
Variables used to impute: gsis_id, season, games, gs, years_of_experience, age, ageCentered20, ageCentered20Quadratic, height, weight, rookie_year, draft_number, fantasy_points, fantasy_points_ppr, fantasyPoints, fantasyPoints_lag, carries, rushing_yards, rushing_tds, rushing_fumbles, rushing_fumbles_lost, rushing_first_downs, rushing_epa, rushing_2pt_conversions, receptions, targets, receiving_yards, receiving_tds, receiving_fumbles, receiving_fumbles_lost, receiving_air_yards, receiving_yards_after_catch, receiving_first_downs, receiving_epa, receiving_2pt_conversions, racr, target_share, air_yards_share, wopr, special_teams_tds, ybc_att.rush, yac_att.rush, att.rush, yds.rush, td.rush, x1d.rush, ybc.rush, yac.rush, brk_tkl.rush, att_br.rush, ybc_r.rec, yac_r.rec, adot.rec, rat.rec, tgt.rec, rec.rec, yds.rec, td.rec, x1d.rec, ybc.rec, yac.rec, brk_tkl.rec, drop.rec, int.rec, drop_percent.rec, rec_br.rec
games yrs_f_ agCn20 agC20Q fntsy_ fnts__ fntsyP carris rshng_y rshng_t rshng_f rshng_fm_ rshng_fr_ rsh_2_ rcptns targts rcvng_y rcvng_t rcvng_f rcvng_fm_ rcvng_r_ rcv___ rcvng_fr_ rcv_2_ spcl__ rcvng_p racr ar_yr_ trgt_s wopr fntsP_ rok_yr drft_n gs att.rs yds.rs td.rsh x1d.rs ybc.rs yc.rsh brk_tkl.rs att_b. tgt.rc rec.rc yds.rc td.rec x1d.rc ybc.rc yac.rc brk_tkl.rc drp.rc int.rc adt.rc rat.rc drp_p. rc_br. ybc_r. yc_r.r rshng_p ybc_t. yc_tt.
iter 1: 0.8094 0.1093 0.0070 0.0035 0.3272 0.0235 0.0052 0.2840 0.1426 0.2634 0.8628 0.7885 0.0924 1.1067 0.0298 0.0611 0.0249 0.0969 0.8177 0.4537 0.0650 0.0804 0.0249 0.9680 1.0235 0.1738 0.5438 0.0868 0.4123 0.1172 0.4597 0.0189 0.6057 0.3973 0.0877 0.0886 0.1516 0.0467 0.0593 0.2086 0.4018 0.6464 0.0296 0.0223 0.0237 0.1062 0.0207 0.0428 0.0579 0.4367 0.4700 0.4818 0.3045 0.1724 0.4722 0.2410 0.2693 0.4025 0.3943 0.4791 0.7521
iter 2: 0.1728 0.1469 0.0179 0.0289 0.0104 0.0039 0.0051 0.0863 0.0763 0.1528 0.7880 0.9474 0.0849 1.0234 0.0193 0.0198 0.0137 0.0915 0.4327 0.4835 0.0238 0.0558 0.0221 0.9617 1.0376 0.1464 0.5141 0.0630 0.1827 0.1062 0.4562 0.0379 0.6361 0.3908 0.0641 0.0767 0.1425 0.0603 0.0747 0.1970 0.3903 0.6647 0.0182 0.0171 0.0165 0.1074 0.0226 0.0332 0.0503 0.4361 0.1170 0.4096 0.2673 0.1862 0.2255 0.2386 0.2793 0.4004 0.3648 0.5070 0.7621
iter 3: 0.1713 0.1447 0.0195 0.0276 0.0104 0.0036 0.0051 0.0796 0.0889 0.1611 0.8505 0.9348 0.0904 1.0447 0.0196 0.0205 0.0134 0.0901 0.4465 0.4867 0.0233 0.0569 0.0222 0.9519 1.0103 0.1457 0.5062 0.0617 0.1698 0.1115 0.4530 0.0382 0.6521 0.3899 0.0665 0.0681 0.1457 0.0647 0.0866 0.2055 0.3919 0.6791 0.0169 0.0169 0.0168 0.1107 0.0213 0.0339 0.0500 0.4315 0.1200 0.4148 0.2745 0.1822 0.1947 0.2205 0.2778 0.4032 0.3639 0.4933 0.7741 missRanger object. Extract imputed data via $data
- best iteration: 2
- best average OOB imputation error: 0.2519559 Code
data_train_te <- seasonalData_lag_te_train_imp$data
data_train_te$fantasyPointsMC_lag <- scale(data_train_te$fantasyPoints_lag, scale = FALSE) # mean-centered
data_train_te_matrix <- data_train_te %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()
seasonalData_lag_te_test_imp <- predict(
object = seasonalData_lag_te_train_imp,
newdata = seasonalData_lag_te_test,
seed = 52242)
data_test_te <- seasonalData_lag_te_test_imp
data_test_te_matrix <- data_test_te %>%
mutate(across(where(is.factor), ~ as.numeric(as.integer(.)))) %>%
as.matrix()19.5 Identify Cores for Parallel Processing
We use the future package (Bengtsson, 2025) for parallel (faster) processing.
19.6 Set up the Cross-Validation Folds
19.6.1 k-Fold Cross-Validation
k-fold cross-validation partitions the data into k folds (subsets). In each of the k iterations, the model is trained on \(k - 1\) folds and is evaluated on the remaining fold. For example, in a 10-fold cross-validation (i.e., \(k = 10\)), as used below, the model is trained 10 times, each time leaving out a different 10% of the data for validation. k-fold cross-validation is widely used because it tends to yield stable estimates of model performance, by balancing bias and variance. It is also computationally efficient, requiring only k model fits to evaluate model performance.
19.6.2 Leave-One-Out (LOO) Cross-Validation
Leave-one-out (LOO) cross-validation partitions the data into n folds, where n is the sample size. In each of the n iterations, the model is trained on \(n - 1\) observations and is evaluated on the one left out. For example, in a LOO cross-validation with 100 players, the model is trained 100 times, each time leaving out a different player for validation. LOO cross-validation is especially useful when the dataset is small—too small to form reliable training sets in k-fold cross-validation (e.g., with \(k = 5\) or \(k = 10\), which divide the sample into 5 or 10 folds, respectively). However, LOO tends to be less computationally efficient because it requires more model fits than k-fold cross-validation. LOO tends to have low bias, producing performance estimates closer to those obtained when fitting the model to the full dataset, because each model is trained on nearly all the data. However, LOO also tends to have high variance in its error estimates, because each validation fold contains only a single observation, making those estimates more sensitive to individual data points.
19.7 Fitting the Traditional Linear Regression Models
We describe linear regression in Chapter 11.
19.7.1 Regression with One Predictor
Code
# Set seed for reproducibility
set.seed(52242)
# Set up Cross-Validation
folds <- folds_kFold
# Define Recipe (Formula)
rec <- recipes::recipe(
fantasyPoints_lag ~ ageCentered20,
data = data_train_qb)
# Define Model
lm_spec <- parsnip::linear_reg() %>%
parsnip::set_engine("lm") %>%
parsnip::set_mode("regression")
# Workflow
lm_wf <- workflows::workflow() %>%
workflows::add_recipe(rec) %>%
workflows::add_model(lm_spec)
# Fit Model with Cross-Validation
cv_results <- tune::fit_resamples(
lm_wf,
resamples = folds,
metrics = yardstick::metric_set(rmse, mae, rsq),
control = tune::control_resamples(save_pred = TRUE)
)
# View Cross-Validation metrics
tune::collect_metrics(cv_results)Code
Code
Code
# Calculate combined range for axes
axis_limits <- range(c(df$pred, df$fantasyPoints_lag), na.rm = TRUE)
ggplot(
df,
aes(
x = pred,
y = fantasyPoints_lag)) +
geom_point(
size = 2,
alpha = 0.6) +
geom_abline(
slope = 1,
intercept = 0,
color = "blue",
linetype = "dashed") +
coord_equal(
xlim = axis_limits,
ylim = axis_limits) +
labs(
title = "Predicted vs Actual Fantasy Points (Test Data)",
x = "Predicted Fantasy Points",
y = "Actual Fantasy Points"
) +
theme_classic() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5)) # horizontal y-axis title
19.7.2 Regression with Multiple Predictors
Code
# Set seed for reproducibility
set.seed(52242)
# Set up Cross-Validation
folds <- folds_kFold
# Define Recipe (Formula)
rec <- recipes::recipe(
fantasyPoints_lag ~ ., # use all predictors
data = data_train_qb %>% select(-gsis_id, -fantasyPointsMC_lag))
# Define Model
lm_spec <- parsnip::linear_reg() %>%
parsnip::set_engine("lm") %>%
parsnip::set_mode("regression")
# Workflow
lm_wf <- workflows::workflow() %>%
workflows::add_recipe(rec) %>%
workflows::add_model(lm_spec)
# Fit Model with Cross-Validation
cv_results <- tune::fit_resamples(
lm_wf,
resamples = folds,
metrics = yardstick::metric_set(rmse, mae, rsq),
control = tune::control_resamples(save_pred = TRUE)
)
# View Cross-Validation metrics
tune::collect_metrics(cv_results)Code
Code
Code
# Calculate combined range for axes
axis_limits <- range(c(df$pred, df$fantasyPoints_lag), na.rm = TRUE)
ggplot(
df,
aes(
x = pred,
y = fantasyPoints_lag)) +
geom_point(
size = 2,
alpha = 0.6) +
geom_abline(
slope = 1,
intercept = 0,
color = "blue",
linetype = "dashed") +
coord_equal(
xlim = axis_limits,
ylim = axis_limits) +
labs(
title = "Predicted vs Actual Fantasy Points (Test Data)",
x = "Predicted Fantasy Points",
y = "Actual Fantasy Points"
) +
theme_classic() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5)) # horizontal y-axis title
19.8 Fitting the Machine Learning Models
19.8.1 Least Absolute Shrinkage and Selection Option (Lasso)
Code
# Set seed for reproducibility
set.seed(52242)
# Set up Cross-Validation
folds <- folds_kFold
# Define Recipe (Formula)
rec <- recipes::recipe(
fantasyPoints_lag ~ ., # use all predictors
data = data_train_qb %>% select(-gsis_id, -fantasyPointsMC_lag))
# Define Model
lasso_spec <-
parsnip::linear_reg(
penalty = tune::tune(),
mixture = 1) %>%
parsnip::set_engine("glmnet")
# Workflow
lasso_wf <- workflows::workflow() %>%
workflows::add_recipe(rec) %>%
workflows::add_model(lasso_spec)
# Define grid of penalties to try (log scale is typical)
penalty_grid <- dials::grid_regular(
dials::penalty(range = c(-4, -1)),
levels = 20)
# Tune the Penalty Parameter
cv_results <- tune::tune_grid(
lasso_wf,
resamples = folds,
grid = penalty_grid,
metrics = yardstick::metric_set(rmse, mae, rsq),
control = tune::control_grid(save_pred = TRUE)
)
# View Cross-Validation metrics
tune::collect_metrics(cv_results)Code
best_penalty <- tune::select_best(cv_results, metric = "mae")
# Finalize Workflow with Best Penalty
final_wf <- tune::finalize_workflow(
lasso_wf,
best_penalty)
# Fit Final Model on Training Data
final_model <- workflows::fit(
final_wf,
data = data_train_qb)
# View Coefficients
final_model %>%
workflows::extract_fit_parsnip() %>%
broom::tidy()Code
Code
# Calculate combined range for axes
axis_limits <- range(c(df$pred, df$fantasyPoints_lag), na.rm = TRUE)
ggplot(
df,
aes(
x = pred,
y = fantasyPoints_lag)) +
geom_point(
size = 2,
alpha = 0.6) +
geom_abline(
slope = 1,
intercept = 0,
color = "blue",
linetype = "dashed") +
coord_equal(
xlim = axis_limits,
ylim = axis_limits) +
labs(
title = "Predicted vs Actual Fantasy Points (Test Data)",
x = "Predicted Fantasy Points",
y = "Actual Fantasy Points"
) +
theme_classic() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5)) # horizontal y-axis title
19.8.2 Ridge Regression
Code
# Set seed for reproducibility
set.seed(52242)
# Set up Cross-Validation
folds <- folds_kFold
# Define Recipe (Formula)
rec <- recipes::recipe(
fantasyPoints_lag ~ ., # use all predictors
data = data_train_qb %>% select(-gsis_id, -fantasyPointsMC_lag))
# Define Model
ridge_spec <-
parsnip::linear_reg(
penalty = tune::tune(),
mixture = 0) %>%
parsnip::set_engine("glmnet")
# Workflow
ridge_wf <- workflows::workflow() %>%
workflows::add_recipe(rec) %>%
workflows::add_model(ridge_spec)
# Define grid of penalties to try (log scale is typical)
penalty_grid <- dials::grid_regular(
dials::penalty(range = c(-4, -1)),
levels = 20)
# Tune the Penalty Parameter
cv_results <- tune::tune_grid(
ridge_wf,
resamples = folds,
grid = penalty_grid,
metrics = yardstick::metric_set(rmse, mae, rsq),
control = tune::control_grid(save_pred = TRUE)
)
# View Cross-Validation metrics
tune::collect_metrics(cv_results)Code
best_penalty <- tune::select_best(cv_results, metric = "mae")
# Finalize Workflow with Best Penalty
final_wf <- tune::finalize_workflow(
ridge_wf,
best_penalty)
# Fit Final Model on Training Data
final_model <- workflows::fit(
final_wf,
data = data_train_qb)
# View Coefficients
final_model %>%
workflows::extract_fit_parsnip() %>%
broom::tidy()Code
Code
# Calculate combined range for axes
axis_limits <- range(c(df$pred, df$fantasyPoints_lag), na.rm = TRUE)
ggplot(
df,
aes(
x = pred,
y = fantasyPoints_lag)) +
geom_point(
size = 2,
alpha = 0.6) +
geom_abline(
slope = 1,
intercept = 0,
color = "blue",
linetype = "dashed") +
coord_equal(
xlim = axis_limits,
ylim = axis_limits) +
labs(
title = "Predicted vs Actual Fantasy Points (Test Data)",
x = "Predicted Fantasy Points",
y = "Actual Fantasy Points"
) +
theme_classic() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5)) # horizontal y-axis title
19.8.3 Elastic Net
Code
# Set seed for reproducibility
set.seed(52242)
# Set up Cross-Validation
folds <- folds_kFold
# Define Recipe (Formula)
rec <- recipes::recipe(
fantasyPoints_lag ~ ., # use all predictors
data = data_train_qb %>% select(-gsis_id, -fantasyPointsMC_lag))
# Define Model
enet_spec <-
parsnip::linear_reg(
penalty = tune::tune(),
mixture = tune::tune()) %>%
parsnip::set_engine("glmnet")
# Workflow
enet_wf <- workflows::workflow() %>%
workflows::add_recipe(rec) %>%
workflows::add_model(enet_spec)
# Define a regular grid for both penalty and mixture
grid_enet <- dials::grid_regular(
dials::penalty(range = c(-4, -1)),
dials::mixture(range = c(0, 1)),
levels = c(20, 5) # 20 penalty values × 5 mixture values
)
# Tune the Grid
cv_results <- tune::tune_grid(
enet_wf,
resamples = folds,
grid = grid_enet,
metrics = yardstick::metric_set(rmse, mae, rsq),
control = tune::control_grid(save_pred = TRUE)
)
# View Cross-Validation metrics
tune::collect_metrics(cv_results)Code
best_penalty <- tune::select_best(cv_results, metric = "mae")
# Finalize Workflow with Best Penalty
final_wf <- tune::finalize_workflow(
enet_wf,
best_penalty)
# Fit Final Model on Training Data
final_model <- workflows::fit(
final_wf,
data = data_train_qb)
# View Coefficients
final_model %>%
workflows::extract_fit_parsnip() %>%
broom::tidy()Code
Code
# Calculate combined range for axes
axis_limits <- range(c(df$pred, df$fantasyPoints_lag), na.rm = TRUE)
ggplot(
df,
aes(
x = pred,
y = fantasyPoints_lag)) +
geom_point(
size = 2,
alpha = 0.6) +
geom_abline(
slope = 1,
intercept = 0,
color = "blue",
linetype = "dashed") +
coord_equal(
xlim = axis_limits,
ylim = axis_limits) +
labs(
title = "Predicted vs Actual Fantasy Points (Test Data)",
x = "Predicted Fantasy Points",
y = "Actual Fantasy Points"
) +
theme_classic() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5)) # horizontal y-axis title
19.8.4 Random Forest Machine Learning
19.8.4.1 Cross-Sectional Data
Code
# Set seed for reproducibility
set.seed(52242)
# Set up Cross-Validation
folds <- folds_kFold
# Define Recipe (Formula)
rec <- recipes::recipe(
fantasyPoints_lag ~ ., # use all predictors
data = data_train_qb %>% select(-gsis_id, -fantasyPointsMC_lag))
# Define Model
rf_spec <-
parsnip::rand_forest(
mtry = tune::tune(),
min_n = tune::tune(),
trees = 500) %>%
parsnip::set_mode("regression") %>%
parsnip::set_engine(
"ranger",
importance = "impurity")
# Workflow
rf_wf <- workflows::workflow() %>%
workflows::add_recipe(rec) %>%
workflows::add_model(rf_spec)
# Create Grid
n_predictors <- recipes::prep(rec) %>%
recipes::juice() %>%
dplyr::select(-fantasyPoints_lag) %>%
ncol()
# Dynamically define ranges based on data
rf_params <- hardhat::extract_parameter_set_dials(rf_spec) %>%
dials:::update.parameters(
mtry = dials::mtry(range = c(1L, n_predictors)),
min_n = dials::min_n(range = c(2L, 10L))
)
rf_grid <- dials::grid_random(rf_params, size = 15) #dials::grid_regular(rf_params, levels = 5)
# Tune the Grid
cv_results <- tune::tune_grid(
rf_wf,
resamples = folds,
grid = rf_grid,
metrics = yardstick::metric_set(rmse, mae, rsq),
control = tune::control_grid(save_pred = TRUE)
)
# View Cross-Validation metrics
tune::collect_metrics(cv_results)Code
best_penalty <- tune::select_best(cv_results, metric = "mae")
# Finalize Workflow with Best Penalty
final_wf <- tune::finalize_workflow(
rf_wf,
best_penalty)
# Fit Final Model on Training Data
final_model <- workflows::fit(
final_wf,
data = data_train_qb)
# View Feature Importance
rf_fit <- final_model %>%
workflows::extract_fit_parsnip()
rf_fitparsnip model object
Ranger result
Call:
ranger::ranger(x = maybe_data_frame(x), y = y, mtry = min_cols(~8L, x), num.trees = ~500, min.node.size = min_rows(~3L, x), importance = ~"impurity", num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
Type: Regression
Number of trees: 500
Sample size: 1582
Number of independent variables: 73
Mtry: 8
Target node size: 3
Variable importance mode: impurity
Splitrule: variance
OOB prediction error (MSE): 6292.873
R squared (OOB): 0.5165346 season games gs
158486.2281 311601.5550 508011.8724
years_of_experience age ageCentered20
153382.2301 267708.3635 279843.6026
ageCentered20Quadratic height weight
275022.6554 94161.2553 191252.7328
rookie_year draft_number fantasy_points
140253.5439 272494.7684 1419670.4892
fantasy_points_ppr fantasyPoints completions
1293483.9743 1312301.3737 827216.4193
attempts passing_yards passing_tds
646841.5869 917999.3053 857088.9137
passing_interceptions sacks_suffered sack_yards_lost
130485.7228 161120.2954 220249.8113
sack_fumbles sack_fumbles_lost passing_air_yards
76489.5596 52392.6083 299007.8777
passing_yards_after_catch passing_first_downs passing_epa
329812.9334 939795.2571 431328.9812
passing_cpoe passing_2pt_conversions pacr
177761.1008 35523.1896 168910.0901
carries rushing_yards rushing_tds
240997.7562 181847.7875 57066.7128
rushing_fumbles rushing_fumbles_lost rushing_first_downs
54868.5816 33249.7074 118475.4108
rushing_epa rushing_2pt_conversions special_teams_tds
212956.2609 26187.3427 585.1349
pocket_time.pass pass_attempts.pass throwaways.pass
117380.4026 542011.1159 181719.6877
spikes.pass drops.pass bad_throws.pass
56148.3695 317853.6846 331561.0155
times_blitzed.pass times_hurried.pass times_hit.pass
347944.2411 194260.1925 192719.6026
times_pressured.pass batted_balls.pass on_tgt_throws.pass
441632.1558 92205.6330 362699.7319
rpo_plays.pass rpo_yards.pass rpo_pass_att.pass
161827.5832 166390.5923 141437.2823
rpo_pass_yards.pass rpo_rush_att.pass rpo_rush_yards.pass
134159.9270 64700.4095 108135.7323
pa_pass_att.pass pa_pass_yards.pass drop_pct.pass
274342.4437 247080.0647 160183.4104
bad_throw_pct.pass on_tgt_pct.pass pressure_pct.pass
195925.8780 132741.7748 167263.9786
ybc_att.rush yac_att.rush att.rush
169961.6625 155900.6714 229358.3533
yds.rush td.rush x1d.rush
158863.0638 65818.0750 155380.6876
ybc.rush yac.rush brk_tkl.rush
156091.4085 153192.0666 49373.0228
att_br.rush
60286.9067 Code
Code
# Calculate combined range for axes
axis_limits <- range(c(df$pred, df$fantasyPoints_lag), na.rm = TRUE)
ggplot(
df,
aes(
x = pred,
y = fantasyPoints_lag)) +
geom_point(
size = 2,
alpha = 0.6) +
geom_abline(
slope = 1,
intercept = 0,
color = "blue",
linetype = "dashed") +
coord_equal(
xlim = axis_limits,
ylim = axis_limits) +
labs(
title = "Predicted vs Actual Fantasy Points (Test Data)",
x = "Predicted Fantasy Points",
y = "Actual Fantasy Points"
) +
theme_classic() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5)) # horizontal y-axis title
Code
Now we can stop the parallel backend:
19.8.4.2 Longitudinal Data
Approaches to estimating random forest models with longitudinal data are described by Hu & Szymczak (2023). Below, we fit longitudinal random forest models using the MERF() function of the LongituRF package (Capitaine, 2020).
Code
smerf <- LongituRF::MERF(
X = data_train_qb %>% dplyr::select(season:att_br.rush) %>% as.matrix(), # predictors of the fixed effects
Y = data_train_qb[,c("fantasyPoints_lag")] %>% as.matrix(), # outcome variable
Z = data_train_qb %>% dplyr::mutate(constant = 1) %>% dplyr::select(constant, passing_yards, passing_tds, passing_interceptions, passing_epa, pacr) %>% as.matrix(), # predictors of the random effects
id = data_train_qb[,c("gsis_id")] %>% as.matrix(), # player ID (for nesting)
time = data_train_qb[,c("ageCentered20")] %>% as.matrix(), # time variable
ntree = 500,
sto = "BM")[1] "stopped after 11 iterations."
Call:
randomForest(x = X, y = ystar, ntree = ntree, mtry = mtry, importance = TRUE)
Type of random forest: regression
Number of trees: 500
No. of variables tried at each split: 25
Mean of squared residuals: 165.2049
% Var explained: 98.49 [1] 154.9823 110.8246 105.2359 108.3674 127.9415 145.0449 145.5596 158.7728
[9] 160.9756 151.7372 165.2049Code
# Predict on Test Data
predict(
smerf,
X = data_test_qb %>% dplyr::select(season:att_br.rush) %>% as.matrix(),
Z = data_train_qb %>% dplyr::mutate(constant = 1) %>% dplyr::select(constant, passing_yards, passing_tds, passing_interceptions, passing_epa, pacr) %>% as.matrix(),
id = data_test_qb[,c("gsis_id")] %>% as.matrix(),
time = data_test_qb[,c("ageCentered20")] %>% as.matrix())Error in Z[w, , drop = FALSE] %*% object$random_effects[k, ]: non-conformable arguments
19.8.5 Combining Tree-Boosting with Mixed Models
To combine tree-boosting with mixed models, we use the gpboost package (Sigrist et al., 2025).
Adapted from here: https://towardsdatascience.com/mixed-effects-machine-learning-for-longitudinal-panel-data-with-gpboost-part-iii-523bb38effc
19.8.5.1 Process Data
If using a gamma distribution, it requires positive-only values:
19.8.5.2 Specify Predictor Variables
19.8.5.3 Specify General Model Options
19.8.5.4 Identify Optimal Tuning Parameters
For identifying the optimal tuning parameters for boosting, we partition the training data into inner training data and validation data. We randomly split the training data into 80% inner training data and 20% held-out validation data. We then use the mean absolute error as our index of prediction accuracy on the held-out validation data.
Code
# Partition training data into inner training data and validation data
ntrain_qb <- dim(data_train_qb_matrix)[1]
set.seed(52242)
valid_tune_idx_qb <- sample.int(ntrain_qb, as.integer(0.2*ntrain_qb)) #
folds_qb <- list(valid_tune_idx_qb)
# Specify parameter grid, gp_model, and gpb.Dataset
param_grid_qb <- list(
"learning_rate" = c(0.2, 0.1, 0.05, 0.01), # the step size used when updating predictions after each boosting round (high values make big updates, which can speed up learning but risk overshooting; low values are usually more accurate but require more rounds)
"max_depth" = c(3, 5, 7), # maximum depth (levels) of each decision tree; deeper trees capture more complex patterns and interactions but risk overfitting; shallower trees tend to generalize better
"min_data_in_leaf" = c(10, 50, 100), # minimum number of training examples in a leaf node; higher values = more regularization (simpler trees)
"lambda_l2" = c(0, 1, 5)) # L2 regularization penalty for large weights in tree splits; adds a "cost" for complexity; helps prevent overfitting by shrinking the contribution of each tree
other_params_qb <- list(
num_leaves = 2^6) # maximum number of leaves per tree; controls the maximum complexity of each tree (along with max_depth); more leaves = more expressive models, but can overfit if min_data_in_leaf is too small; num_leaves must be consistent with max_depth, because deeper trees naturally support more leaves; max is: 2^n, where n is the largest max_depth
gp_model_qb <- gpboost::GPModel(
group_data = data_train_qb_matrix[,"gsis_id"],
likelihood = model_likelihood,
group_rand_coef_data = cbind(
data_train_qb_matrix[,"ageCentered20"],
data_train_qb_matrix[,"ageCentered20Quadratic"]),
ind_effect_group_rand_coef = c(1,1))
gp_data_qb <- gpboost::gpb.Dataset(
data = data_train_qb_matrix[,pred_vars_qb],
categorical_feature = pred_vars_qb_categorical,
label = data_train_qb_matrix[,"fantasyPoints_lag"]) # could instead use mean-centered variable (fantasyPointsMC_lag) and add mean back afterward
# Find optimal tuning parameters
opt_params_qb <- gpboost::gpb.grid.search.tune.parameters(
param_grid = param_grid_qb,
params = other_params_qb,
num_try_random = NULL,
folds = folds_qb,
data = gp_data_qb,
gp_model = gp_model_qb,
nrounds = nrounds,
early_stopping_rounds = 50, # stops training early if the model hasn’t improved on the validation set in 50 rounds; prevents overfitting and saves time
verbose_eval = 1,
metric = "mae")Error in fd$booster$update(fobj = fobj): [GPBoost] [Fatal] Inf occured in gradient wrt covariance / auxiliary parameter number 3 (counting starts at 1, total nb. par. = 4) Error: object 'opt_params_qb' not foundA learning rate of 1 is very high for boosting. Even if a learning rate of 1 did well in tuning, I use a lower learning rate (0.1) to avoid overfitting. I also added some light regularization (lambda_l2) for better generalization. I also set the maximum tree depth (max_depth) at 5 to capture complex (up to 5-way) interactions, and set the maximum number of terminal nodes (num_leaves) per tree at 2^5 (32). I set the minimum number of samples in any leaf (min_data_in_leaf) to be 10.
19.8.5.5 Specify Model and Tuning Parameters
Code
gp_model_qb <- gpboost::GPModel(
group_data = data_train_qb_matrix[,"gsis_id"],
likelihood = model_likelihood,
group_rand_coef_data = cbind(
data_train_qb_matrix[,"ageCentered20"],
data_train_qb_matrix[,"ageCentered20Quadratic"]),
ind_effect_group_rand_coef = c(1,1))
gp_data_qb <- gpboost::gpb.Dataset(
data = data_train_qb_matrix[,pred_vars_qb],
categorical_feature = pred_vars_qb_categorical,
label = data_train_qb_matrix[,"fantasyPoints_lag"])
params_qb <- list(
learning_rate = 0.1,
max_depth = 5,
min_data_in_leaf = 10,
lambda_l2 = 1,
num_leaves = 2^5,
num_threads = num_cores)
nrounds_qb <- 123 # identify optimal number of trees through iteration and cross-validation
#gp_model_qb$set_optim_params(params = list(optimizer_cov = "nelder_mead")) # to speed up model estimation19.8.5.6 Fit Model
Code
[GPBoost] [Info] Total Bins 8709
[GPBoost] [Info] Number of data points in the train set: 1582, number of used features: 73
[GPBoost] [Info] [GPBoost with gamma likelihood]: initscore=4.805531
[GPBoost] [Info] Start training from score 4.80553119.8.5.7 Model Results
=====================================================
Covariance parameters (random effects):
Param.
Group_1 0
Group_1_rand_coef_nb_1 0
Group_1_rand_coef_nb_2 0
-----------------------------------------------------
Additional parameters:
Param.
shape 0.8186
=====================================================
19.8.5.8 Evaluate Accuracy of Model on Test Data
Code
# Test Model on Test Data
pred_test_qb <- predict(
gp_model_fit_qb,
data = data_test_qb_matrix[,pred_vars_qb],
group_data_pred = data_test_qb_matrix[,"gsis_id"],
group_rand_coef_data_pred = cbind(
data_test_qb_matrix[,"ageCentered20"],
data_test_qb_matrix[,"ageCentered20Quadratic"]),
predict_var = FALSE,
pred_latent = FALSE)
y_pred_test_qb <- pred_test_qb[["response_mean"]] # if outcome is mean-centered, add mean(data_train_qb_matrix[,"fantasyPoints_lag"])
predictedVsActual <- data.frame(
predictedPoints = y_pred_test_qb,
actualPoints = data_test_qb_matrix[,"fantasyPoints_lag"]
)
predictedVsActualCode
19.8.5.9 Generate Predictions for Next Season
Code
# Generate model predictions for next season
pred_nextYear_qb <- predict(
gp_model_fit_qb,
data = newData_qb_matrix[,pred_vars_qb],
group_data_pred = newData_qb_matrix[,"gsis_id"],
group_rand_coef_data_pred = cbind(
newData_qb_matrix[,"ageCentered20"],
newData_qb_matrix[,"ageCentered20Quadratic"]),
predict_var = FALSE,
pred_latent = FALSE)
newData_qb$fantasyPoints_lag <- pred_nextYear_qb$response_mean
# Merge with player names
newData_qb <- left_join(
newData_qb,
nfl_playerIDs %>% select(gsis_id, name),
by = "gsis_id"
)
newData_qb %>%
arrange(-fantasyPoints_lag) %>%
select(name, fantasyPoints_lag, fantasyPoints)19.9 Conclusion
19.10 Session Info
R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: UTC
tzcode source: system (glibc)
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] glmnet_4.1-9 Matrix_1.7-3 lubridate_1.9.4 forcats_1.0.0
[5] stringr_1.5.1 readr_2.1.5 tidyverse_2.0.0 effectsize_1.0.1
[9] gpboost_1.5.8 R6_2.6.1 LongituRF_0.9 yardstick_1.3.2
[13] workflowsets_1.1.1 workflows_1.2.0 tune_1.3.0 tidyr_1.3.1
[17] tibble_3.3.0 rsample_1.3.0 recipes_1.3.1 purrr_1.0.4
[21] parsnip_1.3.2 modeldata_1.4.0 infer_1.0.9 ggplot2_3.5.2
[25] dplyr_1.1.4 dials_1.4.0 scales_1.4.0 broom_1.0.8
[29] tidymodels_1.3.0 powerjoin_0.1.0 missRanger_2.6.1 future_1.58.0
[33] petersenlab_1.1.6
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 shape_1.4.6.1 rstudioapi_0.17.1
[4] jsonlite_2.0.0 datawizard_1.1.0 magrittr_2.0.3
[7] TH.data_1.1-3 estimability_1.5.1 farver_2.1.2
[10] nloptr_2.2.1 rmarkdown_2.29 vctrs_0.6.5
[13] minqa_1.2.8 base64enc_0.1-3 sparsevctrs_0.3.4
[16] htmltools_0.5.8.1 Formula_1.2-5 parallelly_1.45.0
[19] htmlwidgets_1.6.4 sandwich_3.1-1 plyr_1.8.9
[22] zoo_1.8-14 emmeans_1.11.1 lifecycle_1.0.4
[25] iterators_1.0.14 pkgconfig_2.0.3 fastmap_1.2.0
[28] rbibutils_2.3 digest_0.6.37 colorspace_2.1-1
[31] furrr_0.3.1 Hmisc_5.2-3 labeling_0.4.3
[34] latex2exp_0.9.6 randomForest_4.7-1.2 RJSONIO_2.0.0
[37] timechange_0.3.0 compiler_4.5.1 withr_3.0.2
[40] htmlTable_2.4.3 backports_1.5.0 DBI_1.2.3
[43] psych_2.5.6 MASS_7.3-65 lava_1.8.1
[46] tools_4.5.1 pbivnorm_0.6.0 ranger_0.17.0
[49] foreign_0.8-90 future.apply_1.20.0 nnet_7.3-20
[52] doFuture_1.1.1 glue_1.8.0 quadprog_1.5-8
[55] nlme_3.1-168 grid_4.5.1 checkmate_2.3.2
[58] cluster_2.1.8.1 reshape2_1.4.4 generics_0.1.4
[61] gtable_0.3.6 tzdb_0.5.0 class_7.3-23
[64] hms_1.1.3 data.table_1.17.6 foreach_1.5.2
[67] pillar_1.10.2 mitools_2.4 splines_4.5.1
[70] lhs_1.2.0 lattice_0.22-7 FNN_1.1.4.1
[73] survival_3.8-3 tidyselect_1.2.1 mix_1.0-13
[76] knitr_1.50 reformulas_0.4.1 gridExtra_2.3
[79] stats4_4.5.1 xfun_0.52 hardhat_1.4.1
[82] timeDate_4041.110 stringi_1.8.7 DiceDesign_1.10
[85] yaml_2.3.10 boot_1.3-31 evaluate_1.0.4
[88] codetools_0.2-20 cli_3.6.5 rpart_4.1.24
[91] xtable_1.8-4 parameters_0.26.0 Rdpack_2.6.4
[94] lavaan_0.6-19 Rcpp_1.1.0 globals_0.18.0
[97] coda_0.19-4.1 gower_1.0.2 bayestestR_0.16.1
[100] GPfit_1.0-9 lme4_1.1-37 listenv_0.9.1
[103] viridisLite_0.4.2 mvtnorm_1.3-3 ipred_0.9-15
[106] prodlim_2025.04.28 insight_1.3.1 rlang_1.1.6
[109] multcomp_1.4-28 mnormt_2.1.1